{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "hw9_unsupervised.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Iallen520/lhy_DL_Hw/blob/master/hw9_unsupervised.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r1_Q26m0LN2r",
        "colab_type": "text"
      },
      "source": [
        "This is the tutorial of **Image Clustering**\n",
        "<br>\n",
        "If you want to skip the **training** phase, please refer to the **clustering** section directly.\n",
        "<br>\n",
        "**Training** required sections:  Prepare Training Data, Model, Training\n",
        "<br>\n",
        "**Clustering** required sections: Prepare Training Data, Model, Dimension Reduction & Clustering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GUyppmxhsgJg",
        "colab_type": "text"
      },
      "source": [
        "同學們也可以利用提供的wget指令下載訓練資料，並自行mount到雲端資料夾上，如作業一所示。這邊就不再贅述<br>\n",
        "作業的第一部分是要訓練一個autoencoder以抽取好的圖片表徵，第二部分則是將抽出來的表徵降維到二維，以便我們利用分群的方法獲得我們的答案<br>\n",
        "\n",
        "若有任何問題，歡迎來信至助教信箱 ntu-ml-2020spring-ta@googlegroups.com"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "br18nlnhJ7cr",
        "colab_type": "text"
      },
      "source": [
        "# Download Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vjd8-9s3J6oE",
        "colab_type": "code",
        "outputId": "227400d6-ccd4-40e5-e98c-ff88a13a9da3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "!gdown --id '1BZb2AqOHHaad7Mo82St1qTBaXo_xtcUc' --output trainX.npy \n",
        "!gdown --id '152NKCpj8S_zuIx3bQy0NN5oqpvBjdPIq' --output valX.npy \n",
        "!gdown --id '1_hRGsFtm5KEazUg2ZvPZcuNScGF-ANh4' --output valY.npy \n",
        "!mkdir checkpoints\n",
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1BZb2AqOHHaad7Mo82St1qTBaXo_xtcUc\n",
            "To: /content/trainX.npy\n",
            "26.1MB [00:00, 46.2MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=152NKCpj8S_zuIx3bQy0NN5oqpvBjdPIq\n",
            "To: /content/valX.npy\n",
            "100% 1.54M/1.54M [00:00<00:00, 83.1MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1_hRGsFtm5KEazUg2ZvPZcuNScGF-ANh4\n",
            "To: /content/valY.npy\n",
            "100% 4.13k/4.13k [00:00<00:00, 19.1MB/s]\n",
            "checkpoints  sample_data  trainX.npy  valX.npy\tvalY.npy\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w8WjVvaONQ-m",
        "colab_type": "text"
      },
      "source": [
        "# Prepare Training Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NrIsy5olK1sH",
        "colab_type": "text"
      },
      "source": [
        "定義我們的 preprocess：將圖片的數值介於 0~255 的 int 線性轉為 0～1 的 float。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xXTyAnhzHzHP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def preprocess(image_list):\n",
        "    \"\"\" Normalize Image and Permute (N,H,W,C) to (N,C,H,W)\n",
        "    Args:\n",
        "      image_list: List of images (9000, 32, 32, 3)\n",
        "    Returns:\n",
        "      image_list: List of images (9000, 3, 32, 32)\n",
        "    \"\"\"\n",
        "    image_list = np.array(image_list)\n",
        "    image_list = np.transpose(image_list, (0, 3, 1, 2))\n",
        "    image_list = (image_list / 255.0) * 2 - 1\n",
        "    image_list = image_list.astype(np.float32)\n",
        "    return image_list"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qj_hairpGhLj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch.utils.data import Dataset\n",
        "\n",
        "class Image_Dataset(Dataset):\n",
        "    def __init__(self, image_list):\n",
        "        self.image_list = image_list\n",
        "    def __len__(self):\n",
        "        return len(self.image_list)\n",
        "    def __getitem__(self, idx):\n",
        "        images = self.image_list[idx]\n",
        "        return images"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8EvJPEeGLgt7",
        "colab_type": "text"
      },
      "source": [
        "將訓練資料讀入，並且 preprocess。\n",
        "之後我們將 preprocess 完的訓練資料變成我們需要的 dataset。請同學不要使用 valX 和 valY 來訓練。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_tMv9S5oqn9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "trainX = np.load('trainX.npy')\n",
        "trainX_preprocessed = preprocess(trainX)\n",
        "img_dataset = Image_Dataset(trainX_preprocessed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z64cTA5jaNtg",
        "colab_type": "text"
      },
      "source": [
        "# Some useful functions\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wCtxq6GSL4tq",
        "colab_type": "text"
      },
      "source": [
        "這邊提供一些有用的 functions。\n",
        "一個是計算 model 參數量的（report 會用到），另一個是固定訓練的隨機種子（以便 reproduce）。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KWJNJs-UaUFb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import random\n",
        "import torch\n",
        "\n",
        "def count_parameters(model, only_trainable=False):\n",
        "    if only_trainable:\n",
        "        return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
        "    else:\n",
        "        return sum(p.numel() for p in model.parameters())\n",
        "\n",
        "def same_seeds(seed):\n",
        "    torch.manual_seed(seed)\n",
        "    if torch.cuda.is_available():\n",
        "        torch.cuda.manual_seed(seed)\n",
        "        torch.cuda.manual_seed_all(seed)  # if you are using multi-GPU.\n",
        "    np.random.seed(seed)  # Numpy module.\n",
        "    random.seed(seed)  # Python random module.\n",
        "    torch.backends.cudnn.benchmark = False\n",
        "    torch.backends.cudnn.deterministic = True\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_ZuRV_dNjhD",
        "colab_type": "text"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mxBEwGYMSjm",
        "colab_type": "text"
      },
      "source": [
        "定義我們的 baseline autoeocoder。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dci5VCIuQwvI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.nn as nn\n",
        "\n",
        "class AE(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(AE, self).__init__()\n",
        "        \n",
        "        self.encoder = nn.Sequential(\n",
        "            nn.Conv2d(3, 64, 3, stride=1, padding=1),\n",
        "            nn.ReLU(True),\n",
        "            nn.MaxPool2d(2),\n",
        "            nn.Conv2d(64, 128, 3, stride=1, padding=1),\n",
        "            nn.ReLU(True),\n",
        "            nn.MaxPool2d(2),\n",
        "            nn.Conv2d(128, 256, 3, stride=1, padding=1),\n",
        "            nn.ReLU(True),\n",
        "            nn.MaxPool2d(2)\n",
        "        )\n",
        " \n",
        "        self.decoder = nn.Sequential(\n",
        "            nn.ConvTranspose2d(256, 128, 5, stride=1),\n",
        "            nn.ReLU(True),\n",
        "            nn.ConvTranspose2d(128, 64, 9, stride=1),\n",
        "            nn.ReLU(True),\n",
        "            nn.ConvTranspose2d(64, 3, 17, stride=1),\n",
        "            nn.Tanh()\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x1 = self.encoder(x)\n",
        "        x  = self.decoder(x1)\n",
        "        return x1, x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mF_7fi7xM5Er",
        "colab_type": "text"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p8Wr2tNYNgcP",
        "colab_type": "text"
      },
      "source": [
        "這個部分就是主要的訓練階段。\n",
        "我們先將準備好的 dataset 當作參數餵給 dataloader。\n",
        "將 dataloader、model、loss criterion、optimizer 都準備好之後，就可以開始訓練。\n",
        "訓練完成後，我們會將 model 存下來。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uKZ9rhK-2b76",
        "colab_type": "code",
        "outputId": "ea7e3428-a8f2-4d24-eb42-c55639591c1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import torch\n",
        "from torch import optim\n",
        "\n",
        "\n",
        "model = AE().cuda()\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=1e-5, weight_decay=1e-5)\n",
        "\n",
        "model.train()\n",
        "n_epoch = 100\n",
        "\n",
        "same_seeds(0)\n",
        "# 準備 dataloader, model, loss criterion 和 optimizer\n",
        "img_dataloader = DataLoader(img_dataset, batch_size=64, shuffle=True)\n",
        "\n",
        "\n",
        "# 主要的訓練過程\n",
        "for epoch in range(n_epoch):\n",
        "    for data in img_dataloader:\n",
        "        img = data\n",
        "        img = img.cuda()\n",
        "\n",
        "        output1, output = model(img)\n",
        "        loss = criterion(output, img)\n",
        "        \n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        if (epoch+1) % 10 == 0:\n",
        "            torch.save(model.state_dict(), './checkpoints/checkpoint_{}.pth'.format(epoch+1))\n",
        "            \n",
        "    print('epoch [{}/{}], loss:{:.5f}'.format(epoch+1, n_epoch, loss.data))\n",
        "\n",
        "# 訓練完成後儲存 model\n",
        "torch.save(model.state_dict(), './checkpoints/last_checkpoint.pth')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch [1/100], loss:0.18878\n",
            "epoch [2/100], loss:0.14376\n",
            "epoch [3/100], loss:0.12757\n",
            "epoch [4/100], loss:0.10704\n",
            "epoch [5/100], loss:0.10452\n",
            "epoch [6/100], loss:0.09946\n",
            "epoch [7/100], loss:0.10202\n",
            "epoch [8/100], loss:0.08631\n",
            "epoch [9/100], loss:0.07889\n",
            "epoch [10/100], loss:0.08150\n",
            "epoch [11/100], loss:0.08935\n",
            "epoch [12/100], loss:0.08037\n",
            "epoch [13/100], loss:0.07993\n",
            "epoch [14/100], loss:0.07547\n",
            "epoch [15/100], loss:0.07523\n",
            "epoch [16/100], loss:0.07289\n",
            "epoch [17/100], loss:0.06453\n",
            "epoch [18/100], loss:0.06986\n",
            "epoch [19/100], loss:0.07062\n",
            "epoch [20/100], loss:0.07040\n",
            "epoch [21/100], loss:0.07914\n",
            "epoch [22/100], loss:0.06411\n",
            "epoch [23/100], loss:0.06729\n",
            "epoch [24/100], loss:0.07044\n",
            "epoch [25/100], loss:0.05336\n",
            "epoch [26/100], loss:0.05535\n",
            "epoch [27/100], loss:0.06062\n",
            "epoch [28/100], loss:0.06091\n",
            "epoch [29/100], loss:0.05718\n",
            "epoch [30/100], loss:0.06311\n",
            "epoch [31/100], loss:0.05550\n",
            "epoch [32/100], loss:0.05895\n",
            "epoch [33/100], loss:0.05204\n",
            "epoch [34/100], loss:0.05809\n",
            "epoch [35/100], loss:0.05163\n",
            "epoch [36/100], loss:0.05603\n",
            "epoch [37/100], loss:0.05748\n",
            "epoch [38/100], loss:0.05249\n",
            "epoch [39/100], loss:0.05345\n",
            "epoch [40/100], loss:0.05329\n",
            "epoch [41/100], loss:0.05438\n",
            "epoch [42/100], loss:0.05511\n",
            "epoch [43/100], loss:0.04452\n",
            "epoch [44/100], loss:0.05176\n",
            "epoch [45/100], loss:0.04840\n",
            "epoch [46/100], loss:0.04514\n",
            "epoch [47/100], loss:0.04849\n",
            "epoch [48/100], loss:0.04236\n",
            "epoch [49/100], loss:0.04588\n",
            "epoch [50/100], loss:0.04712\n",
            "epoch [51/100], loss:0.04399\n",
            "epoch [52/100], loss:0.05763\n",
            "epoch [53/100], loss:0.04315\n",
            "epoch [54/100], loss:0.04394\n",
            "epoch [55/100], loss:0.05243\n",
            "epoch [56/100], loss:0.04318\n",
            "epoch [57/100], loss:0.04572\n",
            "epoch [58/100], loss:0.05333\n",
            "epoch [59/100], loss:0.04735\n",
            "epoch [60/100], loss:0.05158\n",
            "epoch [61/100], loss:0.04218\n",
            "epoch [62/100], loss:0.04303\n",
            "epoch [63/100], loss:0.04771\n",
            "epoch [64/100], loss:0.04533\n",
            "epoch [65/100], loss:0.04715\n",
            "epoch [66/100], loss:0.04752\n",
            "epoch [67/100], loss:0.03926\n",
            "epoch [68/100], loss:0.04381\n",
            "epoch [69/100], loss:0.04774\n",
            "epoch [70/100], loss:0.04514\n",
            "epoch [71/100], loss:0.04071\n",
            "epoch [72/100], loss:0.03735\n",
            "epoch [73/100], loss:0.03897\n",
            "epoch [74/100], loss:0.04302\n",
            "epoch [75/100], loss:0.03927\n",
            "epoch [76/100], loss:0.04554\n",
            "epoch [77/100], loss:0.04188\n",
            "epoch [78/100], loss:0.03979\n",
            "epoch [79/100], loss:0.04184\n",
            "epoch [80/100], loss:0.04500\n",
            "epoch [81/100], loss:0.04051\n",
            "epoch [82/100], loss:0.04414\n",
            "epoch [83/100], loss:0.03764\n",
            "epoch [84/100], loss:0.03588\n",
            "epoch [85/100], loss:0.04563\n",
            "epoch [86/100], loss:0.04065\n",
            "epoch [87/100], loss:0.04453\n",
            "epoch [88/100], loss:0.04513\n",
            "epoch [89/100], loss:0.04127\n",
            "epoch [90/100], loss:0.04320\n",
            "epoch [91/100], loss:0.04463\n",
            "epoch [92/100], loss:0.03595\n",
            "epoch [93/100], loss:0.04440\n",
            "epoch [94/100], loss:0.04116\n",
            "epoch [95/100], loss:0.03759\n",
            "epoch [96/100], loss:0.03764\n",
            "epoch [97/100], loss:0.03290\n",
            "epoch [98/100], loss:0.03654\n",
            "epoch [99/100], loss:0.03459\n",
            "epoch [100/100], loss:0.04128\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HhU5gcRhlTE1"
      },
      "source": [
        "# Dimension Reduction & Clustering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jrn7UhtLyB4n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def cal_acc(gt, pred):\n",
        "    \"\"\" Computes categorization accuracy of our task.\n",
        "    Args:\n",
        "      gt: Ground truth labels (9000, )\n",
        "      pred: Predicted labels (9000, )\n",
        "    Returns:\n",
        "      acc: Accuracy (0~1 scalar)\n",
        "    \"\"\"\n",
        "    # Calculate Correct predictions\n",
        "    correct = np.sum(gt == pred)\n",
        "    acc = correct / gt.shape[0]\n",
        "    # 因為是 binary unsupervised clustering，因此取 max(acc, 1-acc)\n",
        "    return max(acc, 1-acc)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hl9skAvMOvSV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_scatter(feat, label, savefig=None):\n",
        "    \"\"\" Plot Scatter Image.\n",
        "    Args:\n",
        "      feat: the (x, y) coordinate of clustering result, shape: (9000, 2)\n",
        "      label: ground truth label of image (0/1), shape: (9000,)\n",
        "    Returns:\n",
        "      None\n",
        "    \"\"\"\n",
        "    X = feat[:, 0]\n",
        "    Y = feat[:, 1]\n",
        "    plt.scatter(X, Y, c = label)\n",
        "    plt.legend(loc='best')\n",
        "    if savefig is not None:\n",
        "        plt.savefig(savefig)\n",
        "    plt.show()\n",
        "    return"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c0QvyGtKOp_p",
        "colab_type": "text"
      },
      "source": [
        "接著我們使用訓練好的 model，來預測 testing data 的類別。\n",
        "\n",
        "由於 testing data 與 training data 一樣，因此我們使用同樣的 dataset 來實作 dataloader。與 training 不同的地方在於 shuffle 這個參數值在這邊是 False。\n",
        "\n",
        "準備好 model 與 dataloader，我們就可以進行預測了。\n",
        "\n",
        "我們只需要 encoder 的結果（latents），利用 latents 進行 clustering 之後，就可以分類了。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DBnn6RlncV-j",
        "colab_type": "code",
        "outputId": "4f3da19c-9f55-4204-92e8-3ee2a5c24698",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "import torch\n",
        "from sklearn.decomposition import KernelPCA\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.cluster import MiniBatchKMeans\n",
        "\n",
        "def inference(X, model, batch_size=256):\n",
        "    X = preprocess(X)\n",
        "    dataset = Image_Dataset(X)\n",
        "    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)\n",
        "    latents = []\n",
        "    for i, x in enumerate(dataloader):\n",
        "        x = torch.FloatTensor(x)\n",
        "        vec, img = model(x.cuda())\n",
        "        if i == 0:\n",
        "            latents = vec.view(img.size()[0], -1).cpu().detach().numpy()\n",
        "        else:\n",
        "            latents = np.concatenate((latents, vec.view(img.size()[0], -1).cpu().detach().numpy()), axis = 0)\n",
        "    print('Latents Shape:', latents.shape)\n",
        "    return latents\n",
        "\n",
        "def predict(latents):\n",
        "    # First Dimension Reduction\n",
        "    transformer = KernelPCA(n_components=200, kernel='rbf', n_jobs=-1)\n",
        "    kpca = transformer.fit_transform(latents)\n",
        "    print('First Reduction Shape:', kpca.shape)\n",
        "\n",
        "    # # Second Dimesnion Reduction\n",
        "    X_embedded = TSNE(n_components=2).fit_transform(kpca)\n",
        "    print('Second Reduction Shape:', X_embedded.shape)\n",
        "\n",
        "    # Clustering\n",
        "    pred = MiniBatchKMeans(n_clusters=2, random_state=0).fit(X_embedded)\n",
        "    pred = [int(i) for i in pred.labels_]\n",
        "    pred = np.array(pred)\n",
        "    return pred, X_embedded\n",
        "\n",
        "def invert(pred):\n",
        "    return np.abs(1-pred)\n",
        "\n",
        "def save_prediction(pred, out_csv='prediction.csv'):\n",
        "    with open(out_csv, 'w') as f:\n",
        "        f.write('id, label\\n')\n",
        "        for i, p in enumerate(pred):\n",
        "            f.write(f'{i},{p}\\n')\n",
        "    print(f'Save prediction to {out_csv}.')\n",
        "\n",
        "# load model\n",
        "model = AE().cuda()\n",
        "model.load_state_dict(torch.load('./checkpoints/last_checkpoint.pth'))\n",
        "model.eval()\n",
        "\n",
        "# 準備 data\n",
        "trainX = np.load('trainX.npy')\n",
        "\n",
        "# 預測答案\n",
        "latents = inference(X=trainX, model=model)\n",
        "pred, X_embedded = predict(latents)\n",
        "\n",
        "# 將預測結果存檔，上傳 kaggle\n",
        "save_prediction(pred, 'prediction.csv')\n",
        "\n",
        "# 由於是 unsupervised 的二分類問題，我們只在乎有沒有成功將圖片分成兩群\n",
        "# 如果上面的檔案上傳 kaggle 後正確率不足 0.5，只要將 label 反過來就行了\n",
        "save_prediction(invert(pred), 'prediction_invert.csv')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Latents Shape: (8500, 4096)\n",
            "First Reduction Shape: (8500, 200)\n",
            "Second Reduction Shape: (8500, 2)\n",
            "Save prediction to prediction.csv.\n",
            "Save prediction to prediction_invert.csv.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "snHP0s9kciEn",
        "colab_type": "text"
      },
      "source": [
        "Problem 1.b (作圖)\n",
        "===\n",
        "將 val data 的降維結果 (embedding) 與他們對應的 label 畫出來。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iyNe4gUEcAhZ",
        "colab_type": "code",
        "outputId": "84031586-97c8-4164-fe64-858b0bb52fd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        }
      },
      "source": [
        "valX = np.load('valX.npy')\n",
        "valY = np.load('valY.npy')\n",
        "\n",
        "# ==============================================\n",
        "#  我們示範 basline model 的作圖，\n",
        "#  report 請同學另外還要再畫一張 improved model 的圖。\n",
        "# ==============================================\n",
        "model.load_state_dict(torch.load('./checkpoints/last_checkpoint.pth'))\n",
        "model.eval()\n",
        "latents = inference(valX, model)\n",
        "pred_from_latent, emb_from_latent = predict(latents)\n",
        "acc_latent = cal_acc(valY, pred_from_latent)\n",
        "print('The clustering accuracy is:', acc_latent)\n",
        "print('The clustering result:')\n",
        "plot_scatter(emb_from_latent, valY, savefig='p1_baseline.png')\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Latents Shape: (500, 4096)\n",
            "First Reduction Shape: (500, 200)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Second Reduction Shape: (500, 2)\n",
            "The clustering accuracy is: 0.758\n",
            "The clustering result:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4Icpqkfjrd39uX1Uhi3AKvZL/seOPuhnNh5UGkRxvQgiBKcGkWN7P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ht7D/PeXcT3X/7EpBtf4KzhlxHJUnOdL4rKC7nr/evZfPggDIeBw+Vgh323ZfzUizPvPDx7azbK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\nGOvkLRaLpYz5f6Q499/sBj0OAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vd10OPt5cOqi",
        "colab_type": "text"
      },
      "source": [
        "Problem 2\n",
        "===\n",
        "使用你 test accuracy 最高的 autoencoder，從 trainX 中，取出 index 1, 2, 3, 6, 7, 9 這 6 張圖片\n",
        "畫出他們的原圖以及 reconstruct 之後的圖片。\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1OYYPEqcCpl",
        "colab_type": "code",
        "outputId": "107e2ffa-747d-4b8e-8389-2433ee921b52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# 畫出原圖\n",
        "plt.figure(figsize=(10,4))\n",
        "indexes = [1,2,3,6,7,9]\n",
        "imgs = trainX[indexes,]\n",
        "for i, img in enumerate(imgs):\n",
        "    plt.subplot(2, 6, i+1, xticks=[], yticks=[])\n",
        "    plt.imshow(img)\n",
        "\n",
        "# 畫出 reconstruct 的圖\n",
        "inp = torch.Tensor(trainX_preprocessed[indexes,]).cuda()\n",
        "latents, recs = model(inp)\n",
        "recs = ((recs+1)/2 ).cpu().detach().numpy()\n",
        "recs = recs.transpose(0, 2, 3, 1)\n",
        "for i, img in enumerate(recs):\n",
        "    plt.subplot(2, 6, 6+i+1, xticks=[], yticks=[])\n",
        "    plt.imshow(img)\n",
        "  \n",
        "plt.tight_layout()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kmflEuruEEMnywhrnWVzk9JOdm7I0BbYLf77xgGUZG49JYOUcSzHyDBxycCxb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\n",
            "text/plain": [
              "<Figure size 720x288 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hNhjaEnOcdtR",
        "colab_type": "text"
      },
      "source": [
        "Problem 3\n",
        "===\n",
        "在 autoencoder 的訓練過程中，至少挑選 10 個 checkpoints \n",
        "請用 model 的 train reconstruction error 對 val accuracy 作圖\n",
        "簡單說明你觀察到的現象\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IAc9Ha55cDOr",
        "colab_type": "code",
        "outputId": "66f252df-78c0-4e88-94d6-3de776496cbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import glob\n",
        "checkpoints_list = sorted(glob.glob('checkpoints/checkpoint_*.pth'))\n",
        "\n",
        "# load data\n",
        "dataset = Image_Dataset(trainX_preprocessed)\n",
        "dataloader = DataLoader(dataset, batch_size=64, shuffle=False)\n",
        "\n",
        "points = []\n",
        "with torch.no_grad():\n",
        "    for i, checkpoint in enumerate(checkpoints_list):\n",
        "        print('[{}/{}] {}'.format(i+1, len(checkpoints_list), checkpoint))\n",
        "        model.load_state_dict(torch.load(checkpoint))\n",
        "        model.eval()\n",
        "        err = 0\n",
        "        n = 0\n",
        "        for x in dataloader:\n",
        "            x = x.cuda()\n",
        "            _, rec = model(x)\n",
        "            err += torch.nn.MSELoss(reduction='sum')(x, rec).item()\n",
        "            n += x.flatten().size(0)\n",
        "        print('Reconstruction error (MSE):', err/n)\n",
        "        latents = inference(X=valX, model=model)\n",
        "        pred, X_embedded = predict(latents)\n",
        "        acc = cal_acc(valY, pred)\n",
        "        print('Accuracy:', acc)\n",
        "        points.append((err/n, acc))\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1/10] checkpoints/checkpoint_10.pth\n",
            "Reconstruction error (MSE): 0.08006916330374923\n",
            "Latents Shape: (500, 4096)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/extmath.py:530: RuntimeWarning: invalid value encountered in multiply\n",
            "  v *= signs[:, np.newaxis]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "First Reduction Shape: (500, 200)\n",
            "Second Reduction Shape: (500, 2)\n",
            "Accuracy: 0.688\n",
            "[2/10] checkpoints/checkpoint_100.pth\n",
            "Reconstruction error (MSE): 0.0363836856729844\n",
            "Latents Shape: (500, 4096)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/extmath.py:530: RuntimeWarning: invalid value encountered in multiply\n",
            "  v *= signs[:, np.newaxis]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "First Reduction Shape: (500, 200)\n",
            "Second Reduction Shape: (500, 2)\n",
            "Accuracy: 0.82\n",
            "[3/10] checkpoints/checkpoint_20.pth\n",
            "Reconstruction error (MSE): 0.06317807010575836\n",
            "Latents Shape: (500, 4096)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/extmath.py:530: RuntimeWarning: invalid value encountered in multiply\n",
            "  v *= signs[:, np.newaxis]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "First Reduction Shape: (500, 200)\n",
            "Second Reduction Shape: (500, 2)\n",
            "Accuracy: 0.746\n",
            "[4/10] checkpoints/checkpoint_30.pth\n",
            "Reconstruction error (MSE): 0.054553224600997624\n",
            "Latents Shape: (500, 4096)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/extmath.py:530: RuntimeWarning: invalid value encountered in multiply\n",
            "  v *= signs[:, np.newaxis]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "First Reduction Shape: (500, 200)\n",
            "Second Reduction Shape: (500, 2)\n",
            "Accuracy: 0.758\n",
            "[5/10] checkpoints/checkpoint_40.pth\n",
            "Reconstruction error (MSE): 0.04949435735216328\n",
            "Latents Shape: (500, 4096)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/extmath.py:530: RuntimeWarning: invalid value encountered in multiply\n",
            "  v *= signs[:, np.newaxis]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "First Reduction Shape: (500, 200)\n",
            "Second Reduction Shape: (500, 2)\n",
            "Accuracy: 0.768\n",
            "[6/10] checkpoints/checkpoint_50.pth\n",
            "Reconstruction error (MSE): 0.04551652960683785\n",
            "Latents Shape: (500, 4096)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/extmath.py:530: RuntimeWarning: invalid value encountered in multiply\n",
            "  v *= signs[:, np.newaxis]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "First Reduction Shape: (500, 200)\n",
            "Second Reduction Shape: (500, 2)\n",
            "Accuracy: 0.768\n",
            "[7/10] checkpoints/checkpoint_60.pth\n",
            "Reconstruction error (MSE): 0.042984861448699355\n",
            "Latents Shape: (500, 4096)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/extmath.py:530: RuntimeWarning: invalid value encountered in multiply\n",
            "  v *= signs[:, np.newaxis]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "First Reduction Shape: (500, 200)\n",
            "Second Reduction Shape: (500, 2)\n",
            "Accuracy: 0.74\n",
            "[8/10] checkpoints/checkpoint_70.pth\n",
            "Reconstruction error (MSE): 0.04088168695861218\n",
            "Latents Shape: (500, 4096)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/extmath.py:530: RuntimeWarning: invalid value encountered in multiply\n",
            "  v *= signs[:, np.newaxis]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "First Reduction Shape: (500, 200)\n",
            "Second Reduction Shape: (500, 2)\n",
            "Accuracy: 0.744\n",
            "[9/10] checkpoints/checkpoint_80.pth\n",
            "Reconstruction error (MSE): 0.03903665518293194\n",
            "Latents Shape: (500, 4096)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/extmath.py:530: RuntimeWarning: invalid value encountered in multiply\n",
            "  v *= signs[:, np.newaxis]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "First Reduction Shape: (500, 200)\n",
            "Second Reduction Shape: (500, 2)\n",
            "Accuracy: 0.782\n",
            "[10/10] checkpoints/checkpoint_90.pth\n",
            "Reconstruction error (MSE): 0.037426005456961835\n",
            "Latents Shape: (500, 4096)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/extmath.py:530: RuntimeWarning: invalid value encountered in multiply\n",
            "  v *= signs[:, np.newaxis]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "First Reduction Shape: (500, 200)\n",
            "Second Reduction Shape: (500, 2)\n",
            "Accuracy: 0.746\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vIXEr5jsFUh",
        "colab_type": "code",
        "outputId": "0004de34-ebf8-4f39-a0f9-1e097d0a871e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "ps = list(zip(*points))\n",
        "plt.figure(figsize=(6,6))\n",
        "plt.subplot(211, title='Reconstruction error (MSE)').plot(ps[0])\n",
        "plt.subplot(212, title='Accuracy (val)').plot(ps[1])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2HzzGe2uLWLymiD++t4n/fn8zZw6MZWJaFOP7RzMmJZI+AVozUK6lCcCJ+kX0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\n",
            "text/plain": [
              "<Figure size 432x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}